Don’t Just Collect Location Data, Get Location Intelligence
Businesses used to only interact with their clients face-to-face, where they could get to know what their buyers like and what their company doesn’t offer that they’d like to have.
But now, with the boom of e-commerce — especially on mobile devices — this question has gotten so much more complicated. Virtually every customer is carrying around in their pocket all the data a company needs to refine their marketing and sales efforts using location-based information. Imagine how much data on location businesses like ride-hailing, navigation and social media apps are leveraging to make better decisions for their customers. And with refined GPS chips for mobile devices just a year away, the granularity of this data is only going to grow.
So many companies believe they are nailing it when it comes to analyzing their location data when in reality, they are treating the field similarly to business intelligence. Location Intelligence (LI) today is much more than just gathering information on spatial analysis. It’s being used to provide critical context to data that enables better business decision-making.
But to do so, companies must have the tools in place to reap the rewards of their location data collection, so its analysis is as simple — but insightful — as possible.
What is a Modern LI Tool?
Before BI platforms got visual, user-friendly interfaces that leveraged predictive analytics and artificial intelligence to process raw data in real time, it was little more than a bunch of figures in a spreadsheet. And this is the case now for LI data. 42 percent of businesses are using programs like Excel instead of spatially focused location analysis to understand the ins and outs of their customers. Imagine Magellan or Lewis and Clark foregoing a map to look instead at a series of latitude and longitude numbers in rows and columns and you’ll soon understand why this is an inherently limited way to review spatial data.
And we’re really just at the tip of the iceberg in terms of how we’re collecting and using LI data. The internet of things is poised to have 20.8 billion connected devices by 2020, according to Gartner, a number which does not count smartphones, tablets or computers. Geospatial data coupled with artificial intelligence and machine learning could detect clusters and outliers, predict market volatility and determine future consumer patterns. Businesses will be able to develop new applications or product opportunities based on what they’ve learned. While business stuck in spreadsheets are asking themselves about the health of their stores’ locations today, businesses tapped into LI’s futures are planning out with precision and certainty where their new stores should be tomorrow.
Applications for LI
How can businesses use LI to make decisions right now? The field is already affecting many sectors like retail, emergency management, government and real estate.
LI innovations in retail are going far beyond store planning, though. Businesses are using in-store mapping to determine foot traffic in real time, so they can know where to best place cross promotional products or items that have a higher return on investment. In-store beacons are connecting to customers’ smartphone apps the minute they step in the store, allowing retailers to provide individual-level promotions or personalized discounts based on their preferences. They can also use LI to outmaneuver bad weather to keep store shelves stocked and position distribution centers in optimal locations.
LI is a crucial component of city planning and state and local government response. In emergencies, real-time monitoring can provide instant information to responders, in situations ranging from fires to crime or hurricanes. It can help aid workers distribute their social services in areas where they will have the most efficacy. After floods, like in Houston, city officials could determine which areas are worth rebuilding and which neighborhoods are too dangerous to continue to invest money in after multiple losses.
Outside of emergencies, cities could use this information to determine the best spot for other government services, from public daycares to clinics, providing a smart city advantage to basic, ongoing needs of the public. It could help city officials draw lines for voting districts, ensuring the demographics of those areas are heterogenous and will result in more fair elections.
Realtors will no longer have to pull up manual neighborhood comparisons to determine how to price a house. The process could even become as simple as AirBnB’s model, where it suggests to hosts how much a rental is worth per night based on neighborhood information. Buyers and sellers could could use machine learning to better predict housing trends, so they can get the best deal for them at the ideal time.
The Future of LI
With 84 percent of executives expected to invest in LI tools in the next three years, location is poised to become one of the most prominent and valuable pieces of data businesses analyze. In the future, enterprises will focus on location-based services, providing customers what they want at the exact moment in time — and space — that they want it. This will create significant competitive advantages to the companies that are first at the table, but understanding what modern LI is, and what it isn’t, is step one to making sure you aren’t left behind in the LI revolution.
Leave your comment below, or reply to others.
Please note that this comment section is for thoughtful, on-topic discussions. Admin approval is required for all comments. Your comment may be edited if it contains grammatical errors. Low effort, self-promotional, or impolite comments will be deleted.
Read more from the Meeting of the Minds Blog
Spotlighting innovations in urban sustainability and connected technology
“Historically, government leaders haven’t felt it was in their purview to take action in response to the opioid problem, or to make active decisions about it. What I always say is that ‘opioid misuse is a community problem that requires a community solution.’ There are root issues that lead to the problem, and we must tackle those aspects of the problem in order to really solve it.”
As Meeting of the Minds well knows, the integration of technology in all aspects of city life will manifest in many ways over the next two decades. Artificial intelligence, crowdsourcing, and data collection and analysis have gotten the most attention, but many of the most striking changes are set to occur in the physical realm – the layout of streets and sidewalks. Planners are hard at work right now trying to anticipate what’s going to be needed to accommodate delivery drones, trackless trams, and of course driverless cars and trucks, which will present their own congestion problems potentially, but also will free up all kinds of urban land no longer needed for traffic flow or parking. The transformation of the urban landscape will be more complicated than the transition from horses to cars, but no less doable.
Replacing grass with climate appropriate plants (and irrigating those plants properly) can reduce a landscape’s water needs by 70-80 percent. During the last California drought, we saw homes across the state doing this, a trend significant enough to be clear on Google Maps. This was a big part of why California’s urban communities were able to meet, in fact exceed, the emergency drought mandate of reducing water use by 20 percent.